// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli    Codeplay Software Ltd.
// Ralph Potter  Codeplay Software Ltd.
// Luke Iwanski  Codeplay Software Ltd.
// Contact: <eigen3@codeplay.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_TEST_FUNC                cxx11_tensor_forced_eval_sycl
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_USE_SYCL

#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>

using Eigen::Tensor;

void test_forced_eval_sycl(const Eigen::SyclDevice& sycl_device)
{

    int                     sizeDim1    = 100;
    int                     sizeDim2    = 200;
    int                     sizeDim3    = 200;
    Eigen::array<int, 3>    tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
    Eigen::Tensor<float, 3> in1(tensorRange);
    Eigen::Tensor<float, 3> in2(tensorRange);
    Eigen::Tensor<float, 3> out(tensorRange);

    float* gpu_in1_data = static_cast<float*>(sycl_device.allocate(in1.dimensions().TotalSize() * sizeof(float)));
    float* gpu_in2_data = static_cast<float*>(sycl_device.allocate(in2.dimensions().TotalSize() * sizeof(float)));
    float* gpu_out_data = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize() * sizeof(float)));

    in1 = in1.random() + in1.constant(10.0f);
    in2 = in2.random() + in2.constant(10.0f);

    // creating TensorMap from tensor
    Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in1(gpu_in1_data, tensorRange);
    Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in2(gpu_in2_data, tensorRange);
    Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_out(gpu_out_data, tensorRange);
    sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(), (in1.dimensions().TotalSize()) * sizeof(float));
    sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(), (in1.dimensions().TotalSize()) * sizeof(float));
    /// c=(a+b)*b
    gpu_out.device(sycl_device) = (gpu_in1 + gpu_in2).eval() * gpu_in2;
    sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.dimensions().TotalSize()) * sizeof(float));
    for ( int i = 0; i < sizeDim1; ++i ) {
        for ( int j = 0; j < sizeDim2; ++j ) {
            for ( int k = 0; k < sizeDim3; ++k ) {
                VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) + in2(i, j, k)) * in2(i, j, k));
            }
        }
    }
    printf("(a+b)*b Test Passed\n");
    sycl_device.deallocate(gpu_in1_data);
    sycl_device.deallocate(gpu_in2_data);
    sycl_device.deallocate(gpu_out_data);
}

void test_cxx11_tensor_forced_eval_sycl()
{
    cl::sycl::gpu_selector s;
    Eigen::SyclDevice      sycl_device(s);
    CALL_SUBTEST(test_forced_eval_sycl(sycl_device));
}
